Wireless data communications are subject to significant interference. Receivers and reception methods must overcome the noise to detect communications, acquire code for the communication, and synchronize to the communication. Difficulty increases along with increasing data rates and with decreasing power allocated for the data communication. Of course, high data rates are highly desirable. High data rates permit, for example, a laptop computer to quickly download documents and web pages through a wireless communications on a WiFi network. Cellular handsets, for example also benefit from high data rates. Receivers operating in such devices, however, desirably use little power for a variety of reasons, including extended operational time when operating on a portable power source such as a battery, and to reduce interference created by the receiver that can impact transmission.
A receiver operating in a typical wireless communication scheme experiences low signal to interference and noise ratio (SINR). Signal reception at low SINR poses many challenges. A popular strategy for transmitting and receiving in such an environment is known as spread spectrum communications. Data transmitted by a transmitter in a spread spectrum communication makes use of a clock and a pseudo random noise (PN) sequence to generate spreading code and data symbols. Spread spectrum communication techniques are popular for several reasons including resistance to narrowband interference and jamming, multipath rejection and suitability for multiple access schemes.
Currently deployed spread spectrum based communication networks, for example 802.11b, support high data rates at high signal to interference and noise ratio (SINR) for asynchronous packet-based communication. Asynchronous communications are advantageous since they don't require a separate pilot channel for synchronizing their communication to a pilot signal. In one-to-many downlink transmissions in cellular networks, for example IS-95, mobile handsets synchronize to a pilot signal sent by a base station. The synchronous communication using a pilot channel leads to the loss of power even in the absence of useful communication.
For accurate demodulation in asynchronous packet-based communication, a spread spectrum receiver needs to use the data transmission itself to synchronize to the carrier frequency and phase of the transmitted signal. This is typically achieved by adding a preamble in front of the data bits in a packet. Symbol detection at the receiver requires synchronization to the clock used at the transmitter for generating the spreading code and data symbols. It further requires discovering the start of the spreading sequence in the received signal for correct de-spreading operation at the receiver. Such synchronization involves two separate operations referred to as code acquisition and tracking.
The acquisition operation involves a search for the start of the pseudo random noise (PN) code in the received signal for precise de-spreading of the received signal. The tracking operation performs fine synchronization of the receiver clock to the clock used for generating the data symbols and spreading code at the transmitter. For coherent demodulation, the tracking operation also performs carrier recovery at the receiver. Tracking is performed after code acquisition as the SINR per chip is typically too low to accurately determine chip transitions without leveraging the spreading gain of the code acquisition operation. At low SINR, error correction techniques can be used for reliable estimation of transmitted data symbols even if a significant percentage of raw data symbols are erroneous.
Code acquisition requires testing all the possible hypotheses for correct alignment of the code phase in the received signal with a PN correlation used in the de-spreading operation for data symbol detection. This hypothesis testing is typically carried out in a serial manner as the hardware complexity, cost and size prohibit use of parallel correlators for reducing the acquisition time. Improving very large scale integration (VLSI) technology was long ago recognized as having the potential for a practical implementation of parallel correlator. See, R. L. Pickholtz, D. L. Schilling, and L. B. Milstein, “Theory of spread-spectrum comunications—a tutorial,” IEEE Transactions on Communications, vol. COM-30, no. 5, pp. 855-884, May 1982. Despite a couple decades of improving VLSI technology, the serial correlation approach is still favored.